🛍️ Sales Dataset

📘 Overview

This dataset contains detailed sales transaction records for various products across multiple regions, channels, and customer types. It can be used for sales analysis, business forecasting, dashboard creation, and machine learning applications such as revenue prediction or customer segmentation.

📂 File Information

• Filename: sales.csv
• Total Rows: 2,738
• Total Columns: 8
• File Format: CSV (Comma-Separated Values)
• Encoding: UTF-8

🧩 Columns Description

Column Name	Description
Date	Transaction date in YYYY-MM-DD format.
Product	Name of the product sold (e.g., Almonds, Cashews, Walnuts).
Region	Geographic region where the sale occurred (e.g., East, West, South).
Units_Sold	Number of units sold for the transaction.
Revenue	Total revenue generated (in local currency).
Salesperson	Name of the salesperson responsible for the sale.
Channel	Mode of sale — either Online or Offline.
Customer_Type	Type of customer — typically Retail or Wholesale.

🧠 Example Preview

Date	Product	Region	Units_Sold	Revenue	Salesperson	Channel	Customer_Type
2022-01-01	Almonds	West	37	295.16	Meena	Online	Retail
2022-01-01	Raisins	East	14	218.39	Raj	Online	Wholesale
2022-01-01	Walnuts	West	45	642.71	Anita	Online	Retail

📊 Potential Use Cases

use mnp; #1.Write a query to display total sales revenue for each year. select year(date), sum(Revenue) as total_revenue from sale group by year(date);

#2.Retrieve the top 3 products based on total units sold across all 3 years. select Product, sum(Units_Sold) as total_Unitsold from sale group by Product order by total_Unitsold desc limit 3;

#3.Display year-wise revenue generated from each Sales Channel. select year(date), Channel, sum(Revenue) as total_revenue from sale group by year(date),Channel;

alter table sale add column years int; update sale set years=year(date);

set sql_safe_updates=0; select * from sale;

#4.List the top-performing salesperson for each year based on revenue. select salesperson, years, sum(Revenue) as total_revenue from sale group by years,salesperson;

#5.Using a window function, rank the products by revenue within each region. select Region, Product, sum(Revenue) as total_revenue, rank() over(partition by Region order by sum(Revenue) desc) as rank_wise_region from sale group by Region,Product ;

#6.Display each salesperson’s percent contribution to their region’s total sales using SUM() OVER. select salesperson, region, sum(revenue) as salesperson_revenue, sum(sum(revenue)) over (partition by region) as region_total, round(100 * sum(revenue) / sum(sum(revenue)) over (partition by region),2) as percent_contribution from sale group by salesperson, region;

#7.Find the top-performing region based on total revenue using a subquery. select region, sum(revenue) as total_revenue from sale group by region having sum(revenue) = (select max(region_total) from (select region, sum(revenue) as region_total from sale
group by region
) t);

#8.Create a view that stores average revenue per channel per customer type, and query it to find which combination gives the highest average. create view channel_performance as select channel, customer_type, avg(revenue) as avg_revenue from sale group by channel, customer_type;

#9.Find products that perform better in online than offline channels. select product, sum(case when channel = 'online' then revenue else 0 end) as online_revenue, sum(case when channel = 'offline' then revenue else 0 end) as offline_revenue from sale group by product having online_revenue > offline_revenue;

#10.Identify seasonal trends: find average monthly revenue by product to detect seasonality. select product, date_format(date,'%y-%m') as y_month, avg(revenue) as average_revenue from sale group by product,y_month;

✍️ Author

Created and curated by [Your Name]. Feel free to contribute or report issues in the repository.

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Tags: sales · retail · analytics · forecasting · business-intelligence